[unityByFreedom]

Click-baity. AI tech isn't stuck. There are many forthcoming breakthroughs, particularly in medicine, which should really benefit humanity. Radiology is poised to let CNNs make radiologists a lot more efficient. We just need to build the labeled datasets.

If we invest heavily in some AI tech, let it be to produce huge medical datasets. The software and hardware is ready. We're only lacking sufficient data to make more diagnoses with super-human accuracy.

[sdenton4]

Yeah, where I sit, papers from two years ago are considered ancient, and the advances if the last few years means I can train things on my laptop in for hours that likely would have been a week of gpu time in 2014. This means we can experiment more easily, and try things out with fewer resources, which in turn leads to faster innovation.

So AI isn't stuck. It's also mostly working on well defined, targeted problems.

Life, on the other hand, works towards a very ill-defined objective function (survive, collectively) over millions of years; all of the emergent behavior we're astonished by is maybe just side effects of working on that objective. (This is a crass viewpoint, but let's stick with it for the sake of argument.)

We mostly aren't working on such objective functions, partly because it's hard to compare results, partly because there aren't clear milestones for success (indeed the goal posts for AGI shift as far as AI advances) and partly because skynet.

In fact, we are consistently surprised by the AI we already have. It finds ways to exploit our fitness functions constantly, and fine tricks and heuristics to gain a couple points on the final score constantly. Click bait comes to mind: we want to surface good content, use clicks as a proxy for quality, and get what we see for instead of what we wanted. Which somehow takes us directly to president trump... (Sure your kid can find a cool way to get out of a chair, but call me when she inadvertantly threatens the basis of the US democracy in the process. And then we can talk about the pressing need for AGI.)

[unityByFreedom]

> Life, on the other hand, works towards a very ill-defined objective function (survive, collectively) over millions of years; all of the emergent behavior we're astonished by is maybe just side effects of working on that objective

Agreed. Plus think about all the data and processing power that went into evolution. And some folks think because a system beat a human at Go that we're nearer solving life's age old question, that is, the essence of intelligence.

[pdimitar]

> There are many forthcoming breakthroughs... We just need to build...

Not sure how you don't see the irony. This has probably been said thousands of times for many scientific areas throughout history. Example:

There are forthcoming breakthroughs in humanity being an interstellar civilization. We just need to build faster-than-light engines and terraforming equipment. Nothing major, right?

[unityByFreedom]

> There are forthcoming breakthroughs in humanity being an interstellar civilization. We just need to build faster-than-light engines and terraforming equipment. Nothing major, right?

Building a dataset is easy and not something you would compare to faster-than-light engines. Believe it or not, some major breakthroughs are held back by simple lack of funding, and lack of awareness.

To make a dataset you need to pay radiologists to label enough data for the system to do its job well. This could be thousands, or hundreds of thousands of images. It is technically speaking very doable, but also very expensive. Then there are data privacy issues stopping you from sharing data. These are social issues, not engineering issues.

[pdimitar]

Your reply, while overall correct, still underestimates the problem of "how do we invent AI?". Data-sets shouldn't be "tuned" or "refined" (tems I see in practically every "AI" article); if they need to be remade then the consumer is not only not AI -- it's not even a clever NN implementation.

Forgive my cynicism if you can, but in my eyes you guys just support what might make you money one day (or already does) and thus aren't objective. You're like the parents that are completely blind to their child's defects due to paternal / maternal hormones.

There's no AI on this planet. There are not even beginnings of an AI. Deep learning is practically a statistically biased classification algorithm and not much else.

To me the term AI is being abused. I want AI to exist, but I am seeing every indication that the area is falling victim to capitalistic interests and this won't change anytime soon.

[unityByFreedom]

> Your reply, while overall correct, still underestimates the problem of "how do we invent AI?"

I'm not talking about building a real AI.

I actually agree with you that we're nowhere near developing that. Not sure where you got any other idea from me.

I'm saying there are some machine learning problems that could be served by some simple data entry. This could save lives, including yours and mine, via advanced cancer detection [1]

You're right that since I studied data science, I'm incentivized to advertise its usefulness. But, I studied data science because I believe it is a growing part of our future.

You can try it yourself too. There are many tutorials online. Making use of machine learning gets easier every year.

[1] http://money.cnn.com/2015/03/12/technology/enlitic-technolog...

[pdimitar]

I would gladly take the time, if only I had some. :(

Thank you for your kind answer.

[michaelmrose]

There is pretty good reason to suppose faster than light travel is impossible. Aside from our current understanding of physics there is also the question of where all the aliens are.

If FTL is possible one could see even one intelligent species possessed of such technology spreading over the galaxy over thousands of years. It would also seem decidedly odd to suppose that if faster than light is possible only we are smart enough to invent it.

Removing faster than light travel doesn't remove the question of where are all the aliens but it sure does make it easier to swallow.

[pdimitar]

All true.

(My opinion on where are all the aliens is that collectively speaking, we're little more than ordinary jungle beasts with baseball caps (quote by George Carlin) and we're monitored and evaluated on when is a good time for a first contact. Let's just say we're easily at least a millennia away from that point.)

My point in my parent comment was that the overall schema of assertions like "breakthroughs are incoming" and "we just need to do X" are overly optimistic. So I gave an exaggerated example to demonstrate that point.

[xiphias]

I agree with the fact that AI isn't stuck (flagged the article). I would say the research is ready, but there's still a lot of infrastructure work for integrating with data sets, learning and serving cheaply on high scale. Still, lots of people are making hard to productionize the research results.

[m0dbit]

One group ensures their works are a convoluted mess to maintain their dominance.Another group ensures the computational stack is a convoluted and resource hungry mess to maintain their dominance.

A match made in heaven. Two peas stuck in local minimum pot vacuuming up money and resources.

One pea says : > there's still a lot of infrastructure work for integrating with data sets, learning and serving cheaply on high scale. The other pea says : > Lots of people are making it hard to productionize the research results. Both peas agree : This is how I make my money and stay on-top.

You get what you get for reasons. If neither of them wants to agree their stuck. That's fine with those ushering in the new wave.

Enjoy the party while it lasts.

[mtl_usr]

> Radiology is poised to let CNNs make radiologists a lot more efficient.

I honestly don't see the added value of having a "more efficient" human once the classifier is good enough.

[unityByFreedom]

I'm taking my cues from Jeremy Howard, who believes we probably won't be a world full of data scientists, since data science requires less effort every year as the software and hardware improves. Rather, we'll still have domain experts, and everyone will just know how to apply data science to their field.

The tools get easier to use every year. While I have difficulty imagining my data science job disappearing, I tend to agree with Jeremy that more and more non-CS people are getting comfortable using computers. Programming, as a field of study, will stabilize at some point, and its usefulness will continue flowing into other fields.